Methods and systems for reducing spillover by detecting signal distortion

ABSTRACT

Methods, apparatus, and articles of manufacture for reducing spillover in a media monitoring system are disclosed. An example method includes determining an actual frequency spectrum of the media monitored by a meter, and determining absolute values of differences between amplitudes of corresponding frequency components of the actual frequency spectrum and an expected frequency spectrum, the expected frequency spectrum stored in a database in association with a media identifier corresponding to the media. An example method also includes determining whether spillover occurred based on a summation of the absolute values satisfying a threshold, crediting the media with a media exposure if spillover did not occur.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. application Ser. No.13/791,432, filed Mar. 8, 2013, which is hereby incorporated byreference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to media monitoring and, moreparticularly, to methods and systems for reducing spillover by detectingsignal distortion.

BACKGROUND

Audience measurement of media, such as television, music, movies, radio,Internet websites, streaming media, etc., is typically carried out bymonitoring media exposure of panelists that are statistically selectedto represent particular demographic groups. Using various statisticalmethods, the captured media exposure data is processed to determine thesize and demographic composition of the audience(s) for programs ofinterest. The audience size and demographic information is valuable toadvertisers, broadcasters and/or other entities. For example, audiencesize and demographic information is a factor in the placement ofadvertisements, as well as a factor in valuing commercial time slotsduring a particular program.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example spillovermanager implemented in accordance with the teachings of this disclosureto manage spillover to reduce media monitoring inaccuracies in thesystem.

FIG. 2 illustrates an example implementation of an example mediaidentifying meter of FIG. 1.

FIG. 3A illustrates an example implementation of the example spillovermanager of FIG. 1.

FIG. 3B illustrates an example expected frequency spectrum analyzed bythe example spillover manager of FIG. 3A.

FIG. 3C illustrates an example actual frequency spectrum analyzed by theexample spillover manager of FIG. 3A.

FIG. 4 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example spillovermanager of FIGS. 1 and/or 3.

FIG. 5 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example mediaidentifying meter of FIGS. 1 and/or 2.

FIG. 6 is another flow diagram representative of example machinereadable instructions that may be executed to implement the examplespillover manager of FIGS. 1 and/or 3.

FIG. 7 is a block diagram of an example processor platform that may beused to execute the instructions of FIGS. 4, 5, and/or 6 to implementthe example media identifying meter 106 of FIG. 2, the example spillovermanager of FIG. 3A, and/or, more generally, the example system of FIG.1.

DETAILED DESCRIPTION

Audience measurement companies enlist persons to participate inmeasurement panels. Such persons (e.g., panelists) agree to allow theaudience measurement company to measure their exposure to media (e.g.,television programming, radio programming, Internet, advertising,signage, outdoor advertising, etc.). In order to associate mediamonitoring data (i.e., data reflecting media presentation) with panelistdemographics, the audience measurement company monitors media device(s)and/or panelist(s) using meters.

In some examples, meters (e.g., stationary meters) are placed withand/or near media presentation devices (e.g., televisions, stereos,speakers, computers, etc.) within a home or household. For example, ameter may be placed in a room with a television and another meter may beplaced in a different room with another television. In some examples,personal portable metering devices (PPMs), which are also known asportable metering devices or portable personal (or people) meters, areused to monitor media exposure of panelists. A PPM is an electronicdevice that is typically worn (e.g., clipped to a belt or other apparel)or carried by a panelist. The term “meter” as used herein refersgenerally to stationary meters and/or portable meters.

In general, media identifying meters are configured to use a variety oftechniques to monitor media presentations at media presentation devicesand/or exposure of panelists to media presentations. For example, onetechnique for monitoring media exposure involves detecting or collectingmedia identifying information (e.g., codes (e.g., watermarks),signatures, etc.) from media signals (e.g., audio and/or video signals)that are emitted or presented by media presentation devices.

As media (e.g., content and/or advertisements) is presented, a mediaidentifying meter may receive media signals (e.g., via a microphone)associated with the media and may detect media (e.g., audio and/orvideo) information associated with the media to generate mediamonitoring data. In general, media monitoring data may include anyinformation that is representative of (or associated with) media and/orthat may be used to identify a particular media presentation (e.g., asong, a television program, a movie, a video game, an advertisement,etc.). For example, the media monitoring data may include signaturesthat are collected or generated by the media identifying meter based onthe media, audio codes that are broadcast simultaneously with (e.g.,embedded in) the media, etc. Each media identifying meter may receivedifferent media signals based on the media presented (e.g., tuned) onthe media presentation devices to which panelists are exposed.

Media monitoring systems may also include one or more people meters toidentify panelists in a monitored audience. Identifying the panelists inthe audience allows mapping of their demographics to the media.Panelists provide their demographic information when they agree to bemonitored by the audience measurement system. Any method of peoplemetering may be employed. For example, the people metering may be activein that it requires panelists to self-identify by, for instance,entering an identifier corresponding to their name, or it may be passivein that electronics (e.g., video cameras) may be used to identify and/orcount persons in the audience. See U.S. Pat. No. 7,609,853, which ishereby incorporated by reference herein in its entirety for an examplepeople metering solution.

A panelist home may present unique monitoring challenges to the mediaidentifying meters. For example, a panelist home often includes multiplemedia presentation devices, each configured to present media to specificviewing and/or listening areas located within the home. Known mediaidentifying meters that are located in one of the viewing and/orlistening areas are typically configured to detect any media beingpresented in the viewing and/or listening area and to credit the mediaas having been presented. Thus, known media identifying meters operateon the premise that any media detected by the media identifying meter ismedia that was presented in that particular viewing and/or listeningarea. However, in some cases, a media identifying meter may detect mediathat is emitted by a media presentation device that is not locatedwithin the viewing or listening proximity of a panelist in the room withthe media identifying meter thereby causing the detected media to beimproperly credited to the panelist currently associated with themonitored area (via, for example, a people meter). The ability of themedia identifying meter to detect media being presented outside of theviewing and/or listening proximity of the panelist is referred to as“spillover” because the media being presented outside of the viewingand/or listening proximity of the panelist is “spilling over” into thearea occupied by the media identifying meter and may not actually fallwithin the attention of the panelist. Spillover may occur, for example,when a television in a particular room is powered off, but a mediaidentifying meter associated with that television detects media beingpresented on a media presentation device in a different room of thepanelist home or of an adjacent home (e.g., a neighbor's condominium orapartment). In such an example, the media identifying meter improperlycredits the media as being presented on the media presentation device itmonitors even though no such presentation occurred.

Another effect, referred to as “hijacking,” occurs when a mediaidentifying meter detects different media being presented at multiplemedia presentation devices at the same time. For example, a mediaidentifying meter in a kitchen may detect a particular media programbeing presented on a media presentation device in the kitchen, but themedia identifying meter may also detect a different media program thatis being presented on a different media presentation device in a livingroom. In such an example, the media presented by the media presentationdevice in the living room may, in some cases, have signals thatoverpower or “hijack” the signals associated with the media beingpresented by the media presentation device in the kitchen. As a result,the media identifying meter in the kitchen may inaccurately credit themedia being presented in the living room and fail to credit the mediabeing presented in the kitchen. In some examples, other difficultiessuch as varying volume levels, varying audio/video content type (e.g.,sparse, medium, rich, etc.), varying household transmissioncharacteristics due to open/closed doors, movement and/or placement offurniture, acoustic characteristics of room layouts, wall construction,floor coverings, ceiling heights, etc. may exacerbate these issues and,thus, lead to inaccurate media presentation detection by mediaidentifying meters.

Example methods and systems disclosed herein may be used to manage audiospillover and/or other sources of media monitoring inaccuracies in thecourse of presentations of media to more accurately assess the exposureof panelists to that media. Example methods and systems may be used toprevent audio spillover from adversely affecting results of mediamonitoring. Some example methods and systems analyze media monitoringdata to determine if audio spillover has occurred. In some suchexamples, if audio spillover has not occurred, the media is credited asactual media exposure (e.g., a panelist has been exposed to the media).If audio spillover has occurred, the media is not credited as an actualmedia exposure.

Example methods and systems disclosed herein detect signal spillover byanalyzing signal distortion associated with media presentations (e.g.,signal distortion of audio signal waveforms representative of mediapresentations). Particular media presentations (e.g., signalsrepresentative of particular media content and/or advertisements) haveparticular frequency spectrums associated with them (e.g., a particularfrequency spectrum may be expected from a particular mediapresentation). A frequency spectrum expected from a particular mediapresentation is referred to herein as an expected frequency spectrum. Insome examples, a media identifying meter monitoring a media presentationfrom a proximate media presentation device may analyze a waveform of themedia presentation and determine an actual frequency spectrum of thewaveform. In some examples, the actual frequency spectrum and/or datarepresentative thereof is compared to the expected frequency spectrumand/or data representative thereof to determine if spillover hasoccurred. For example, the actual frequency spectrum may be differentfrom the expected frequency spectrum when the audio has traveled alarger distance than expected between the media identifying meter andthe media presentation device it monitors, the audio has beentransmitted through different rooms (e.g., the signal has bounced off ofwalls, traveled through a wall, a ceiling, or a floor, etc.), etc. Ifthe actual frequency spectrum is similar to the expected frequencyspectrum (e.g., the signal has not been distorted beyond a thresholdrepresentation of spillover), it is determined that spillover has notoccurred. If the actual frequency spectrum is not similar to theexpected frequency spectrum (e.g., the signal has been distorted beyonda threshold representation of spillover), it is determined thatspillover has occurred. In some examples, when it is determined thatspillover has occurred, the media presentation is not credited as anactual media exposure.

An example method disclosed herein includes identifying media based onmedia monitoring data. The media monitoring data is received from afirst media identifying meter associated with a first media presentationdevice. The example method includes identifying an expected frequencyspectrum associated with the media. The example method includescomparing the expected frequency spectrum to an actual frequencyspectrum collected from the media by the first meter to determine ifspillover occurred. The example method includes crediting the media as amedia exposure if spillover did not occur.

An example spillover manager disclosed herein includes a frequencyspectrum comparator to identify an expected frequency spectrum for mediaassociated with media monitoring data received from a meter associatedwith a media presentation device. The example frequency spectrumcomparator is to compare the expected frequency spectrum to an actualfrequency spectrum to determine if spillover occurred. The actualfrequency spectrum is based on a sample of the media collected by themeter. The example spillover manager includes a media creditor to creditthe media with an exposure if spillover did not occur and to not creditthe media with an exposure if spillover did occur.

An example tangible computer readable storage medium disclosed hereincomprises instructions that, when executed, cause a computing device toidentify media associated with media monitoring data. The mediamonitoring data is received from a first meter associated with a firstmedia presentation device. The example instructions cause the computingdevice to identify an expected frequency spectrum associated with themedia. The example instructions cause the computing device to comparethe expected frequency spectrum to an actual frequency spectrumcollected from the media by the first meter to determine if spilloveroccurred. The example instructions cause the computing device to creditthe media as a media exposure if spillover did not occur.

FIG. 1 illustrates an example media monitoring system 100 in an exampleenvironment of use. The example of FIG. 1 includes an example spillovermanager 102 implemented in accordance with the teachings of thisdisclosure to manage spillover to reduce (e.g., prevent) mediamonitoring inaccuracies in the media monitoring system 100. In theillustrated example, a first media identifying meter 106 monitors mediapresented by a first media presentation device 108 in a first room 110and a second media identifying meter 112 monitors media presented on asecond media presentation device 114 in a second room 116. Either orboth of the first and second media presentation devices 108, 114 may be,for example, a television, a radio, a computer, a stereo system, a DVDplayer, a game console, etc. Media may include, for example, any form ofcontent, television programming, radio programming, movies, songs, anyform of advertisements, Internet information such as websites and/orstreaming media, and/or any other video information, audio information,still image information, and/or computer information to which a panelist(e.g., an example panelist 118) may be exposed. While two rooms 110,116, two media presentation devices 108, 114, and two media identifyingmeters 106, 112 are shown in the example of FIG. 1, any number and/ortype(s) of rooms, any number and/or type(s) of media presentationdevices, and/or any number and/or type(s) of meters (including, forexample, people meters) in any configuration and/or spatial relationshipmay be implemented in the example system 100.

In the illustrated example, to respectively monitor media presented onthe first and second media presentation devices 108, 114, the first andsecond media identifying meters 106, 112 process media signals (orportions thereof such as audio portions of the media signals)respectively output by the first and second media presentation devices108, 114 to extract codes and/or metadata, and/or to generate signaturesfor use in identifying the media and/or a station (e.g., a broadcaster)originating the media. The first media identifying meter 106 of theillustrated example is intended to monitor the first media presentationdevice 108 and to not monitor the second media presentation device 114.The second media identifying meter 112 is intended to monitor the secondmedia presentation device 114 and to not monitor the first mediapresentation device 108.

Identification codes, such as watermarks, ancillary codes, etc. may beembedded within or otherwise transmitted with media signals.Identification codes are data that are inserted into media (e.g., audioor video) to uniquely identify broadcasters and/or media (e.g., contentor advertisements), and/or are carried with the media for anotherpurpose such as tuning (e.g., packet identifier headers (“PIDs”) usedfor digital broadcasting). Codes are typically extracted using adecoding operation.

Signatures are a representation of one or more characteristic(s) of themedia signal (e.g., a characteristic of the frequency spectrum of thesignal). Signatures can be thought of as fingerprints. They aretypically not dependent upon insertion of identification codes in themedia, but instead preferably reflect an inherent characteristic of themedia and/or the media signal. Systems to utilize codes and/orsignatures for audience measurement are long known. See, for example,Thomas, U.S. Pat. No. 5,481,294, which is hereby incorporated byreference in its entirety. Codes, metadata, signatures, channelidentifiers (e.g., tuned channel numbers), etc. collected and/orgenerated by the first or second media identifying meters 106, 112 foruse in identifying media and/or a station transmitting media may bereferred to generally as “media monitoring data.”

In the illustrated example, media monitoring data collected by the firstmedia identifying meter 106 and/or the second media identifying meter112 is transferred to the home processing system 104 for furtherprocessing. The first and second media identifying meters 106, 112 maybe communicatively coupled with the home processing system 104 viawireless and/or hardwired communications and may periodically and/oraperiodically communicate collected media monitoring information to thehome processing system 104. People meters 128, 130 may likewise becommunicatively coupled with the home processing system 104 toperiodically and/or aperiodically forward people identification data tothe home processing system 104.

In the illustrated example, the home processing system 104 iscommunicatively coupled to a remotely located central data collectionfacility 120 via a network 122. The example home processing system 104of FIG. 1 transfers collected media monitoring data to the centralfacility 120 for further processing. The central facility 120 of theillustrated example collects and/or stores, for example, mediamonitoring data that is collected by multiple media monitoring devicessuch as, for example, the media identifying meters 106, 112, and/ordemographic information that is collected by people meters, located atmultiple panelist locations. The central facility 120 may be, forexample, a facility associated with an audience measurement entity suchas The Nielsen Company (US), LLC or any affiliate of The Nielsen Company(US), LLC. The central facility 120 of the illustrated example includesa server 124 and a database 126 that may be implemented using anysuitable processor, memory and/or data storage apparatus such as thatshown in FIG. 7. In some examples, the home processing system 104 islocated in the central facility 120.

The network 122 of the illustrated example is used to communicateinformation and/or data between the example home processing system 104and the central facility 120. The network 122 may be implemented usingany type(s) of public and/or private network(s) such as, but not limitedto, the Internet, a telephone network, a cellular network, a local areanetwork (“LAN”), a cable network, and/or a wireless network. To enablecommunication via the network 122, the home processing system 104 of theillustrated example includes a communication interface that enablesconnection to an Ethernet, a digital subscriber line (“DSL”), atelephone line, a coaxial cable, and/or any wireless connection, etc.

Some known methods for measuring media exposure or presentation track orlog media presentations to which a panelist is exposed and award a mediaexposure credit to a media presentation whenever the panelist is in thevicinity of that media presentation. However, some such methods mayproduce inconsistent or inaccurate monitoring results due to spilloverthat occurs. For example, within the example environment illustrated inFIG. 1, spillover may occur when the first media presentation device 108is powered off (e.g., is not presenting media), but the first mediaidentifying meter 106 associated with the first media presentationdevice 108 detects media being presented by the second mediapresentation device 114. In such an example, the first media identifyingmeter 106 will incorrectly credit the media presented at the secondmedia presentation device 114 as being presented to the panelist 118.Recording media data that has spilled over from another space (e.g., theroom 116) may result in an inaccurate representation of the mediapresented to the panelist 118. In some such examples, the panelist 118may not even know or be aware of the media, but the electronics of themedia identifying meter 106 may still be sensitive enough to detect acode in the media presented by the second media presentation device 114.

The spillover manager 102 of the illustrated example is used to managespillover to reduce (e.g., prevent) media monitoring inaccuracies in theexample system 100 of FIG. 1. The example spillover manager 102 of FIG.1 receives media monitoring data from the first example mediaidentifying meter 106 and/or the second example media identifying meter112 and analyzes the media monitoring data to determine if spillover hasoccurred. In the illustrated example, if the example spillover manager102 detects spillover associated with the first media identifying meter106 and/or the second media identifying meter 112, the media identifiedin the media monitoring data is not credited as actual media exposurefor the meter/monitored media presentation device that experienced thespillover and the media monitoring data associated with the uncreditedmedia is discarded and/or marked as invalid. In the illustrated example,if the example spillover manager 102 does not detect spilloverassociated with the first media identifying meter 106 and/or the secondmedia identifying meter 112, the media identified in the mediamonitoring data is credited as actual media exposure(s). In theillustrated example, the spillover manager 102 sends media monitoringdata associated with credited media to the example central facility 120.In some examples, the spillover manager 102 labels portion(s) of themedia monitoring data as either associated with credited or uncreditedmedia and sends the identified media monitoring data to the examplecentral facility 120.

In the illustrated example, the spillover manager 102 detects spilloverby detecting signal distortion associated with media presentations. Thespillover manager 102 of the illustrated example detects signaldistortion by analyzing frequency spectrums associated with mediapresentations (e.g., frequency spectrums of audio signal waveformsrepresentative of media presentations). A frequency spectrum is arepresentation of an audio signal in the frequency domain. Particularmedia presentations (e.g., particular content and/or advertisements)have particular expected frequency spectrums associated with them (e.g.,a particular frequency spectrum may be expected from a particular mediapresentation when the media is received in the same room in which themedia presentation device resides). A frequency spectrum expected from aparticular media presentation may be referred to as an expectedfrequency spectrum. The spillover manager 102 of the illustrated examplestores and/or accesses (e.g., from the central facility 120) expectedfrequency spectrums and/or data representative thereof for use inspillover detection. Expected frequency spectrums may be determinedduring, for example, a training period where frequency spectrums forparticular media presentations are gathered and analyzed for use inspillover detection. Alternatively, expected frequency spectrums may becollected by the entity associated with the central facility and storedin association with an identifier of the media (e.g., a code or asignature) to enable lookup of the same. In some examples, an expectedfrequency spectrum serves as a signature of the corresponding media.

In the illustrated example, the first and second media identifyingmeters 106, 112 receive media signals (e.g., audio) associated withmedia presentations (e.g., via microphones). In the illustrated example,in addition to collecting media monitoring data from the received mediasignals, the example first and second media identifying meters 106, 112analyze audio waveforms of the media signals and determine or calculatefrequency spectrums of the audio waveforms. The frequency spectrumsand/or data representative thereof (e.g., frequency spectrum data)calculated by the example first and second media identifying meters 106,112 are referred to as “actual frequency spectrums” because theyrepresent the frequency spectrums of the audio waveforms after they havebeen presented on the first or second media presentation devices 108,114 and received at the corresponding first and second media identifyingmeters 106, 112. The first and second media identifying meters 106, 112of the illustrated example timestamp the media monitoring data and theactual frequency spectrum data and send the timestamped media monitoringdata and actual frequency spectrum data to the example spillover manager102 for analysis. In some examples, the frequency spectrum data is notgenerated at the media identifying meters 106, 112, but instead isgenerated at the spillover manager 102.

The spillover manager 102 of the illustrated example uses the mediamonitoring data to identify the media presented at the first and/orsecond media presentation device 108, 114. Once the media is identified,the spillover manager 102 of the illustrated example finds the expectedfrequency spectrum for that media (e.g., by using an identifier of theidentified media to access a table storing the expected frequencyspectrums). To determine if spillover occurred, the spillover manager102 of the illustrated example compares the expected frequency spectrum(or data representative thereof) for the identified media to the actualfrequency spectrum (or data representative thereof) generated based onthe data collected by the example first and/or second media identifyingmeter 106, 112. If the actual frequency spectrum is sufficiently similarto the expected frequency spectrum (e.g., the signal was not distorted),the example spillover manager 102 determines that spillover did notoccur for the corresponding media identification event. Thus, theperson(s) (e.g., the panelist 118) identified as present by a firstpeople meter 128 associated with the corresponding media identifyingmeter that collected the data (e.g., the first media identifying meter106/first media presentation device 108 or a second people meter 130associated with the second media identifying meter 112/second mediapresentation device 114) are credited as having been exposed to themedia. If the actual frequency spectrum is not sufficiently similar tothe expected frequency spectrum (e.g., the signal was distorted), theexample spillover manager 102 determines that spillover occurred for thecorresponding media identification event. Thus, the persons (e.g., thepanelist 118) identified as present by the corresponding people meter(e.g., the first people meter 128 or the second people meter 130) arenot credited as having been exposed to the media. In other words, whenthe example spillover manager 102 of FIG. 1 determines that spilloverhas occurred, the media is not credited as actual media exposure at thecorresponding media presentation device (e.g., media presentationdevices 108, 114).

For example, when the first example media identifying meter 106 receivesa media signal, it determines an actual frequency spectrum for thereceived media signal, in addition to collecting media monitoring datafor the received media signal. In such an example, the first mediaidentifying meter 106 sends the actual frequency spectrum and/or datarepresentative thereof and the media monitoring data to the examplespillover manager 102. The example spillover manager 102 identifies themedia (e.g., content or advertisement) from the media monitoring dataand accesses (e.g., looks up in a local database or cache, retrievesfrom a remote database such as a database at the central facility 120)an expected frequency spectrum associated with that media (i.e., themedia identified by the media monitoring data). If the actual frequencyspectrum is similar to the expected frequency spectrum, the examplespillover manager 102 assumes the media was presented on the firstexample media presentation device 108 corresponding to the first mediaidentifying meter 106 (i.e., the media identifying meter that providedthe media monitoring data under analysis) and credits the media as anactual media exposure at the corresponding media presentation device.Thus, the person(s) identified as present by the first people meter 128(e.g., the panelist 118) are credited as having been exposed to themedia. If the actual frequency spectrum is not similar to the expectedfrequency spectrum, the example spillover manager 102 assumes the mediawas not presented on the example media presentation device 108 (e.g.,the media was presented on the media presentation device 114 and themedia signal spilled over to the example media identifying meter 106),and does not credit the media as an actual media exposure (e.g., doesnot credit the media with exposure to the panelist 118).

While the spillover manager 102 of the illustrated example is shownwithin the example home processing system 104, the spillover manager 102may be implemented at the first media identifying meter 106, the secondmedia identifying meter 112, and/or at the central facility 120.

FIG. 2 is a block diagram of an example implementation of the firstand/or second media identifying meters 106, 112 of FIG. 1. The mediaidentifying meter 106, 112 of the illustrated example receives mediasignals (e.g., audio signals) from one or more media presentationdevices (e.g., the first or second media presentation device 108, 114 ofFIG. 1). In the illustrated example, the media identifying meter 106,112 is used to collect media monitoring data (e.g., to extract and/oranalyze codes and/or signatures from media signals output by acorresponding media presentation device 108, 114) and is used todetermine frequency spectrums of the media signals. Thus, the mediaidentifying meter 106, 112 of the illustrated example is used tocollect, aggregate, locally process, and/or transfer media monitoringdata and/or frequency spectrum data (e.g., data representative ofdetermined frequency spectrums) to the spillover manager 102 of FIG. 1.The media identifying meter 106, 112 of the illustrated example includesan example input 202, an example code collector 204, an examplesignature generator 206, example control logic 208, an exampletimestamper 210, an example database 212, an example transmitter 214,and an example frequency spectrum analyzer 216.

In the illustrated example, the input 202 is a microphone exposed toambient sound and serves to collect audio signals output by monitoredmedia presentation devices (e.g., the media presentation device 108). Tocollect media monitoring data associated with the audio signals, theinput 202 of the illustrated example passes a received audio signal tothe code collector 204 and/or the signature generator 206. The codecollector 204 of the illustrated example extracts codes and/or thesignature generator 206 generates signatures from the signal to identifybroadcasters, channels, stations, and/or programs. The control logic 208of the illustrated example is used to control the code collector 204and/or the signature generator 206 to cause collection of a code, asignature, or both a code and a signature. The identified codes and/orsignatures (e.g., the media monitoring data) are timestamped at theexample timestamper 210, are stored in the example database 212, and aretransmitted by the example transmitter 214 to the spillover manager 102at the home processing system 104. Although the example of FIG. 2collects codes and/or signatures from audio signals, codes or signaturescan additionally or alternatively be collected from other portion(s) ofthe signal (e.g., from the video portion).

The input 202 of the illustrated example also passes the received audiosignal to the example frequency spectrum analyzer 216. The frequencyspectrum analyzer 216 of the illustrated example analyzes the receivedaudio signal and determines a frequency spectrum of the received audiosignal. A frequency spectrum is a representation of the received audiosignal in the frequency domain. The example input 202 may collect anaudio signal for a period of time (e.g., ten seconds, one minute, fiveminutes, ten minutes, etc.) to enable the example frequency spectrumanalyzer 216 to analyze the received audio signal and determine thefrequency spectrum of the received audio signals. In some examples, thefrequency spectrum analyzer 216 detects events (e.g., percussive events)that may be represented in the audio signals collected via the exampleinput 202. For example, events that are unrelated to media presentations(e.g., dogs barking, doors slamming, etc.) may be picked up by theexample input 202 and the frequency spectrum analyzer 216 may detectsuch events in the audio signals and remove representations of suchevents from the audio signals prior to and/or during determination ofthe frequency spectrums of the audio signals.

The frequency spectrum determined by the example frequency spectrumanalyzer 216 is referred to as the actual frequency spectrum. The actualfrequency spectrum and/or data representative thereof is timestamped atthe example timestamper 210, stored at the example database 212, andtransmitted by the example transmitter 214 to the example spillovermanager 102 with the media monitoring data.

While an example manner of implementing the media identifying meter 106,112 of FIG. 1 is illustrated in FIG. 2, one or more of the elements,processes and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example input 202, the example code collector 204, theexample signature collector 206, the example control logic 208, theexample timestamper 210, the example database 212, the exampletransmitter 214, the example frequency spectrum analyzer 216, and/or,more generally, the example media identifying meter 106, 112 of FIG. 1may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example input 202, the example code collector 204, theexample signature collector 206, the example control logic 208, theexample timestamper 210, the example database 212, the exampletransmitter 214, the example frequency spectrum analyzer 216, and/or,more generally, the example media identifying meter 106, 112 could beimplemented by one or more circuit(s), programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)),etc. When reading any of the apparatus or system claims of this patentto cover a purely software and/or firmware implementation, at least oneof the example input 202, the example code collector 204, the examplesignature collector 206, the example control logic 208, the exampletimestamper 210, the example database 212, the example transmitter 214,the example frequency spectrum analyzer 216, and/or the example mediaidentifying meter 106, 112 are hereby expressly defined to include atangible computer readable storage device or storage disc such as amemory, DVD, CD, Blu-ray, etc. storing the software and/or firmware.Further still, the example media identifying meter 106, 112 of FIG. 1may include one or more elements, processes and/or devices in additionto, or instead of, those illustrated in FIG. 2, and/or may include morethan one of any or all of the illustrated elements, processes anddevices.

FIG. 3A is a block diagram of an example implementation of the spillovermanager 102 of FIG. 1. The spillover manager 102 of the illustratedexample receives media monitoring data and actual frequency spectrums(and/or data representative thereof) corresponding to the actualfrequency spectrum from one or more media identifying meter(s) (e.g.,the media identifying meters 106, 112 of FIG. 1). In the illustratedexample, the spillover manager 102 uses the media monitoring data andthe actual frequency spectrum data to determine whether spilloveroccurred (e.g., in the example system 100 of FIG. 1) and whetheridentified media programs are to be credited with actual exposure to apanelist. The spillover manager 102 of the illustrated example is usedto transfer credited media monitoring data (e.g., media monitoring dataassociated with credited media programs) to the central facility 120 ofFIG. 1. The spillover manager 102 of the illustrated example includes anexample frequency spectrum comparator 302, an example frequency spectrumdatabase 304, an example media creditor 306, and an example transmitter308.

The frequency spectrum comparator 302 of the illustrated examplereceives media monitoring data and actual frequency spectrum data fromthe media identifying meter(s) (e.g., the first and second mediaidentifying meters 106, 112 of FIG. 1). The frequency spectrumcomparator 302 of the illustrated example uses the example frequencyspectrum database 304 to identify media (e.g., media that was presentedby the first or second media presentation device 108, 114) based on themedia monitoring data and to identify an expected frequency spectrumassociated with the identified media. Particular media programs areidentified in the example frequency spectrum database 304 using themedia monitoring data (e.g., using codes and/or signatures associatedwith the media). The frequency spectrum database 304 of the illustratedexample stores media identifiers (e.g., identifiers of different mediaprograms) along with expected frequency spectrums and/or datarepresentative thereof associated with the media. For example, for eachparticular media program, the example frequency spectrum database 304stores an expected frequency spectrum. Expected frequency spectrums maybe calculated and/or determined at, for example, a central facility(e.g., the central facility 120 of FIG. 1) prior to implementation ofthe example spillover manager 102 in the example system 100 of FIG. 1and/or the spillover manager 102 may be implemented at the centralfacility 120 to process data collected from various meters. Additionallyor alternatively, the frequency spectrum database 304 may be located atthe central facility and the spillover manager 102 may query thedatabase 304 via the network 122.

Once the frequency spectrum comparator 302 obtains the expectedfrequency spectrum associated with the media, the frequency spectrumcomparator 302 of the illustrated example compares the expectedfrequency spectrum to the actual frequency spectrum (e.g., the actualfrequency spectrum received from the media identifying meter(s) for themedia under analysis). An example expected frequency spectrum 301 isillustrated in FIG. 3B and a corresponding example actual frequencyspectrum 303 is illustrated in FIG. 3C. Both the expected frequencyspectrum 301 and the actual frequency spectrum 303 of the illustratedexamples are associated with the same particular media.

If the actual frequency spectrum is sufficiently similar to the expectedfrequency spectrum (e.g., if the signal was not distorted more than apredetermined amount), the frequency spectrum comparator 302 of theillustrated example determines spillover did not occur and instructs theexample media creditor 306 to credit the media as an actual mediaexposure. If the actual frequency spectrum is not sufficiently similarto the expected frequency spectrum (e.g., if the signal was distortedbeyond a predetermined amount), the frequency spectrum comparator 302 ofthe illustrated example determines that spillover did occur andinstructs the example media creditor 306 to not credit the media as anactual media exposure.

In some examples, the frequency spectrum comparator 302 determines thatthe signal was distorted when the actual frequency spectrum was alteredwhen compared with the expected frequency spectrum. In some examples,the frequency spectrum comparator 302 determines that the signal wasdistorted when the actual frequency spectrum (e.g., the actual frequencyspectrum 303 of FIG. 3C) does not include or includes fewer highfrequency elements than the expected frequency spectrum (e.g., theexpected frequency spectrum 301 of FIG. 3B). In some examples, thefrequency spectrum comparator 302 determines that the signal isdistorted when the actual frequency spectrum does not include orincludes fewer mid-frequency elements than the expected frequencyspectrum.

In some examples, to determine if the actual frequency spectrum issufficiently similar to the expected frequency spectrum to concludespillover did not occur, the example frequency spectrum comparator 302calculates a summation of the absolute values of the differences betweenamplitudes of corresponding frequency components of the actual frequencyspectrum (e.g., amplitudes 307 of the frequency spectrum 303 of FIG. 3C)and the expected frequency spectrum (e.g., amplitudes 305 of thefrequency spectrum 301 of FIG. 3B). In such an example, the examplefrequency spectrum comparator 302 compares the summation of the absolutevalues of the differences between the amplitudes to a threshold. If thesummation of the absolute values of the differences between theamplitudes is larger than the threshold, the example frequency spectrumcomparator 302 determines that the actual frequency spectrum is notsufficiently similar to the expected frequency spectrum for the signalto have originated in the same room as the meter that logged the mediaand, thus, that spillover did occur. If the summation of the absolutevalues of the differences between the amplitudes is not larger than thethreshold, the example frequency spectrum comparator 302 determines thatthe actual frequency spectrum is sufficiently similar to the expectedfrequency spectrum to conclude the signal originated from the mediapresentation device in the same room as the meter that detected themedia and, thus, that spillover did not occur. An example equation tocompare a summation of the absolute values of the differences betweenamplitudes of corresponding frequency components of the actual frequencyspectrum and the expected frequency spectrum to a threshold isillustrated below. In the illustrated equation, f_(N) _(A) represents afrequency component of the actual frequency spectrum, f_(N) _(g) is thecorresponding frequency component of the expected frequency spectrum,and T is the threshold.

${\sum\limits_{0}^{N}\;{{F_{N_{A}} - F_{N_{E}}}}} < T$

The media creditor 306 of the illustrated example credits/does notcredit media as actual media exposure based on the output of the examplefrequency spectrum comparator 302. If the example frequency spectrumcomparator 302 determines that spillover did not occur, the mediacreditor 306 of the illustrated example marks the media monitoring dataassociated with the media as credited. If the example frequency spectrumcomparator 302 determines that spillover did occur, the media creditor306 of the illustrated example discards the media monitoring dataassociated with the media. In some examples, rather than discarding themedia monitoring data associated with the media that is not credited,the example media creditor 306 marks the media monitoring dataassociated with the media as uncredited.

The transmitter 308 of the illustrated example transmits the creditedmedia monitoring data to a central facility (e.g., the central facility120 of FIG. 1) for further processing. In some examples, where theexample media creditor 306 does not discard the uncredited mediamonitoring data, the example transmitter 308 transmits the creditedmedia monitoring data and the uncredited media monitoring data to thecentral facility 120 for further processing.

While an example manner of implementing the spillover manager 102 ofFIG. 1 is illustrated in FIG. 3A, one or more of the elements, processesand/or devices illustrated in FIG. 3A may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example frequency spectrum comparator 302, the examplefrequency spectrum database 304, the example media creditor 306, theexample transmitter 308, and/or, more generally, the example spillovermanager 102 of FIG. 1 may be implemented by hardware, software, firmwareand/or any combination of hardware, software and/or firmware. Thus, forexample, any of the example frequency spectrum comparator 302, theexample frequency spectrum database 304, the example media creditor 306,the example transmitter 308, and/or, more generally, the examplespillover manager 102 could be implemented by one or more circuit(s),programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)), etc. When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example frequencyspectrum comparator 302, the example frequency spectrum database 304,the example media creditor 306, the example transmitter 308, and/or theexample spillover manager 102 are hereby expressly defined to include atangible computer readable storage device or storage disc such as amemory, DVD, CD, Blu-ray, etc. storing the software and/or firmware.Further still, the example spillover manager 102 of FIG. 1 may includeone or more elements, processes and/or devices in addition to, orinstead of, those illustrated in FIG. 3A, and/or may include more thanone of any or all of the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the media identifying meter 106, 112 of FIGS. 1 and 2 andthe spillover manager 102 of FIGS. 1 and 3 are shown in FIGS. 4, 5, and6. In this example, the machine readable instructions comprise a programfor execution by a processor such as the processor 712 shown in theexample processor platform 700 discussed below in connection with FIG.7. The program may be embodied in software stored on a tangible computerreadable storage medium such as a CD-ROM, a floppy disk, a hard drive, adigital versatile disk (DVD), a Blu-ray disk, or a memory associatedwith the processor 712, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor 712and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchartsillustrated in FIGS. 4, 5, and 6, many other methods of implementing theexample media identifying meter 106, 112 and the example spillovermanager 102 may alternatively be used. For example, the order ofexecution of the blocks may be changed, and/or some of the blocksdescribed may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 4, 5, and 6 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals. As used herein, “tangible computerreadable storage medium” and “tangible machine readable storage medium”are used interchangeably. Additionally or alternatively, the exampleprocesses of FIGS. 4, 5, and 6 may be implemented using codedinstructions (e.g., computer and/or machine readable instructions)stored on a non-transitory computer and/or machine readable medium suchas a hard disk drive, a flash memory, a read-only memory, a compactdisk, a digital versatile disk, a cache, a random-access memory and/orany other storage device or storage disk in which information is storedfor any duration (e.g., for extended time periods, permanently, forbrief instances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term non-transitory computer readablemedium is expressly defined to include any type of computer readabledevice or disc and to exclude propagating signals. As used herein, whenthe phrase “at least” is used as the transition term in a preamble of aclaim, it is open-ended in the same manner as the term “comprising” isopen ended.

FIG. 4 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example spillovermanager 102 of FIG. 1 to manage audio spillover in the example system100 of FIG. 1. The spillover manager 102 of the illustrated example isused to manage spillover to reduce (e.g., prevent) media monitoringinaccuracies in the system 100.

The example spillover manager 102 determines if media monitoring datahas been received (block 402). The example spillover manager 102 is toreceive media monitoring data from one or more media identifyingmeter(s) (e.g., the first and/or second media identifying meters 106,112 of FIG. 1). The media monitoring data is representative of mediathat has been presented on one or more media presentation device(s)(e.g., the first and/or second media presentation devices 108, 114 ofFIG. 1). Control remains at block 402 until media monitoring data isreceived by the example spillover manager 102).

The example spillover manager 102 of the illustrated example analyzesthe media monitoring data to determine if spillover has occurred (block404). An example method to determine if spillover has occurred isdescribed below with reference to FIG. 6. If the example spillovermanager 102 detects spillover associated with the first and/or secondmedia identifying meters 106, 112 based on the media monitoring data,the media identified in the media monitoring data is not credited as anactual media exposure (block 406) and the media monitoring dataassociated with the uncredited media is discarded (block 408). Controlthen returns to block 402. In some examples, rather than discarding theuncredited media monitoring data, the example spillover manager 102identifies the media monitoring data as uncredited media and exports theuncredited media monitoring data to a central facility (e.g., theexample central facility 120).

If the example spillover manager 102 of the illustrated example does notdetect spillover associated with the first and/or the second mediaidentifying meter 106, 112, the media identified in the media monitoringdata is credited as an actual media exposure (block 410). The examplespillover manager 102 of the illustrated example exports mediamonitoring data associated with credited media to the example centralfacility 120 (block 412). Control then returns to block 402 when theinstructions are complete.

FIG. 5 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example mediaidentifying meter 106, 112 of FIG. 1 to collect media monitoring dataand to determine frequency spectrums. In the illustrated example, tocollect media monitoring data, the media identifying meter 106, 112extracts and/or analyzes codes and/or signatures from data and/orsignals received from one or more media presentation devices (e.g., thefirst and/or the second media presentation devices 108, 114 of FIG. 1).

Initially, the example input 202 obtains a signal (e.g., an audiosignal) from the one or more media presentation devices (e.g., the firstand/or the second media presentation devices 108, 114) (block 502). Theexample control logic 208 determines whether to collect a code orgenerate a signature from the signal obtained at the input 202 (block504). In the illustrated example, either a code is collected or asignature is generated from the signal. In other examples, both a codeand a signature are collected and/or generated.

If a code is to be collected, the example code collector 204 collects acode from the signal obtained at the input 202 (block 506). The examplecode collector 204 passes the collected code(s) to the timestamper 210.If a signature is to be generated, the signature generator 206 generatesa signature from the signal obtained at the input 202 (block 508). Theexample signature generator 206 passes the generated signature(s) to thetimestamper 210.

The example frequency spectrum analyzer 216 of the illustrated exampledetermines a frequency spectrum of the signal obtained at the input 202(block 510). The example frequency spectrum analyzer 216 passes theactual frequency spectrum and/or data representative thereof to theexample timestamper 210. The example timestamper 210 timestamps thecollected codes and/or generated signatures and the actual frequencyspectrums (and/or data representative thereof) (block 512). The exampletimestamper 210 passes the collected codes and/or generated signaturesand the actual frequency spectrums (and/or data representative thereof)to the example database 212. The example database 212 stores thecollected codes and/or generated signatures and the actual frequencyspectrums (and/or data representative thereof) (block 514). The exampletransmitter 214 periodically and/or aperiodically transmits thecollected codes and/or generated signatures and the actual frequencyspectrums (and/or data representative thereof) to the spillover manager102 of FIG. 1. Control then returns to block 502. In some examples, themedia identifying meter 106, 112 collects and timestamps the collectedaudio data, and periodically or aperiodically exports the timestampeddata for analysis by the spillover manager 102 (which may be located atthe panelist site or at the central facility). In such examples, blocks504-510 and 514 are not performed in the media identifying meter 106,112, and blocks 512 and 516 are modified to operate on the receivedsignal (as opposed to on codes, signatures, and/or frequency spectrums).

FIG. 6 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example spillovermanager 102 of FIG. 3A to manage audio spillover in the example system100 of FIG. 1 by analyzing signal distortion based on frequencyspectrums. The spillover manager 102 of the illustrated example is usedto manage spillover to reduce media monitoring inaccuracies in thesystem 100.

The example spillover manager 102 receives media monitoring data andactual frequency spectrums (and/or data representative thereof) from oneor more media identifying meter(s) (e.g., the first and/or second mediaidentifying meters 106, 112 of FIG. 1) (block 602). The examplespillover manager 102 uses the media monitoring data and actualfrequency spectrums (and/or data representative thereof) to determinewhether spillover occurred (e.g., in the example system 100 of FIG. 1)and whether media is to be credited with an actual media exposure event.

The example frequency spectrum comparator 302 uses the example frequencyspectrum database 304 to identify media (e.g., media that was presentedat the first and/or the second media presentation device 108, 114)associated with the media monitoring data (block 604) and to identify anexpected frequency spectrum associated with the identified media (block606). Particular media programs are identified in the example frequencyspectrum database 304 using the media monitoring data (e.g., using codesand/or signatures associated with the media). The example frequencyspectrum database 304 stores media identifiers (e.g., identifiers ofdifferent media programs) along with expected frequency spectrumsassociated with the media.

The example frequency spectrum comparator 302 compares the expectedfrequency spectrum to the actual frequency spectrum (e.g., the actualfrequency spectrum received from the first and/or the second mediaidentifying meter 106, 112) to determine if signal distortion occurred(block 608). The example frequency spectrum comparator 302 determinesthat signal distortion did occur if the actual frequency spectrum is notsufficiently similar to the expected frequency spectrum. The examplefrequency spectrum comparator 302 determines that signal distortion didnot occur if the actual frequency spectrum is sufficiently similar tothe expected frequency spectrum. In some examples, the frequencyspectrum comparator 302 determines that the signal was distorted whenthe actual frequency spectrum was altered by more than a thresholdamount when compared with the expected frequency spectrum. In someexamples, the frequency spectrum comparator 302 determines that thesignal was distorted when the actual frequency spectrum includes fewerhigh frequency elements than the expected frequency spectrum. In someexamples, the frequency spectrum comparator 302 determines that thesignal was distorted when the actual frequency spectrum includes fewermid-frequency elements than the expected frequency spectrum.

In some examples, to determine if signal distortion occurred, theexample frequency spectrum comparator 302 calculates a summation of theabsolute values of the differences between amplitudes of correspondingfrequency components of the actual frequency spectrum and the expectedfrequency spectrum. In such examples, the example frequency spectrumcomparator 302 compares the summation of the absolute values of thedifferences between the amplitudes to a threshold. If the summation ofthe absolute values of the differences between the amplitudes is largerthan the threshold, the example frequency spectrum comparator 302determines that signal distortion did occur and, thus, the collectedmedia data is due to spillover. If the summation of the absolute valuesof the differences between the amplitudes is not larger than thethreshold, the example frequency spectrum comparator 302 determines thatsignal distortion did not occur and, thus, the collected media data isvalid (i.e., not due to spillover).

If the actual frequency spectrum is not sufficiently similar to theexpected frequency spectrum (e.g., if signal distortion did occur)(block 608), the example frequency spectrum comparator 302 determinesthat spillover did occur and instructs the example media creditor 306not to credit the media as an actual media exposure (block 610). If theexample frequency spectrum comparator 302 determines that spillover didoccur, the example media creditor 306 discards the media monitoring dataassociated with the media (block 612). Control then returns to block602. In some examples, rather than discarding the media monitoring dataassociated with the media that is not credited, the example mediacreditor 306 marks the media monitoring data associated with the mediaas uncredited.

If the actual frequency spectrum is similar to the expected frequencyspectrum (e.g., signal distortion did not occur) (block 608), theexample frequency spectrum comparator 302 determines spillover did notoccur and the example media creditor 306 credits the media as an actualmedia exposure (block 614). In particular, the example media creditor306 marks the media monitoring data associated with the media ascredited (block 614). The example transmitter 308 transmits the creditedmedia monitoring data to a central facility (e.g., the central facility120 of FIG. 1) for further processing (block 616). In some examples,where the example media creditor 306 does not discard the uncreditedmedia monitoring data, the example transmitter 308 transmits thecredited media monitoring data and the uncredited media monitoring datato the central facility 120 for further processing (block 616). Controlthen returns to block 602 when the instructions are complete.

The credited media monitoring data is combined with the people meterdata using timestamps to align the two data sources to matchdemographics and audience size data to the credited media exposures.

FIG. 7 is a block diagram of an example processor platform 700 capableof executing the instructions of FIGS. 4, 5, and 6 to implement themedia identifying meter 106, 112 of FIGS. 1 and 2 and the spillovermanager 102 of FIGS. 1 and 3. The processor platform 700 can be, forexample, a server, a personal computer, a mobile device (e.g., a cellphone, a smart phone, a tablet such as an iPad™), a personal digitalassistant (PDA), an Internet appliance, a DVD player, a CD player, adigital video recorder, a Blu-ray player, a gaming console, a personalvideo recorder, a set top box, or any other type of computing device.

The processor platform 700 of the illustrated example includes aprocessor 712. The processor 712 of the illustrated example is hardware.For example, the processor 712 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 712 of the illustrated example includes a local memory 713(e.g., a cache). The processor 712 of the illustrated example is incommunication with a main memory including a volatile memory 714 and anon-volatile memory 716 via a bus 718. The volatile memory 714 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 716 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 714, 716 is controlledby a memory controller.

The processor platform 700 of the illustrated example also includes aninterface circuit 720. The interface circuit 720 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 722 are connectedto the interface circuit 720. The input device(s) 722 permit a user toenter data and commands into the processor 712. The input device(s) canbe implemented by, for example, an audio sensor, a microphone, a camera(still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 724 are also connected to the interfacecircuit 720 of the illustrated example. The output devices 724 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a light emitting diode (LED), a printer and/or speakers).The interface circuit 720 of the illustrated example, thus, typicallyincludes a graphics driver card.

The interface circuit 720 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network726 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 700 of the illustrated example also includes oneor more mass storage devices 728 for storing software and/or data.Examples of such mass storage devices 728 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 732 of FIGS. 4, 5, and 6 may be stored in themass storage device 728, in the volatile memory 714, in the non-volatilememory 716, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method to credit media presented by a mediapresentation device, the method comprising: determining, via aprocessor, an actual frequency spectrum of the media monitored by ameter; determining, via the processor, absolute values of differencesbetween amplitudes of corresponding frequency components of the actualfrequency spectrum and an expected frequency spectrum, the expectedfrequency spectrum stored in a database in association with a mediaidentifier corresponding to the media; determining, via the processor,whether spillover occurred based on a summation of the absolute valuessatisfying a threshold; and crediting, via the processor, the media witha media exposure if spillover did not occur.
 2. The method as defined inclaim 1, wherein the meter is a portable people meter to be worn orcarried by a panelist.
 3. The method as defined in claim 1, wherein theactual frequency spectrum is based on a sample of the media presented bythe media presentation device and collected by the meter.
 4. The methodas defined in claim 3, further including discarding, via the processor,the sample of the media corresponding to the actual frequency spectrumif spillover did occur.
 5. The method as defined in claim 3, furtherincluding; determining, via the processor, whether the actual frequencyspectrum represents a sound not associated with the media; and when theactual frequency spectrum represents the sound not associated with themedia, discarding, via the processor, the sample of the mediacorresponding to the actual frequency spectrum before determining theabsolute values of the differences between the amplitudes of thecorresponding frequency components of the actual frequency spectrum andthe expected frequency spectrum.
 6. The method as defined in claim 1,wherein the expected frequency spectrum is calculated prior todetermining the actual frequency spectrum of the media.
 7. The method asdefined in claim 1, wherein the determining that spillover occurredindicates that the meter is in a different room from the mediapresentation device.
 8. An apparatus to credit media presented by amedia presentation device, the apparatus comprising: a frequencyspectrum analyzer to determine an actual frequency spectrum of the mediamonitored by a meter; a frequency spectrum comparator to: determineabsolute values of differences between amplitudes of correspondingfrequency components of the actual frequency spectrum and an expectedfrequency spectrum, the expected frequency spectrum stored in a databasein association with a media identifier corresponding to the media;determine whether spillover occurred based on a summation of theabsolute values satisfying a threshold; and a media creditor to: creditthe media with a media exposure if spillover did not occur; and notcredit the media with the media exposure if spillover did occur.
 9. Theapparatus as defined in claim 8, wherein the meter is a portable peoplemeter to be worn or carried by a panelist.
 10. The apparatus as definedin claim 8, wherein the actual frequency spectrum is based on a sampleof the media presented by the media presentation device and collected bythe meter.
 11. The apparatus as defined in claim 10, wherein the mediacreditor is further to discard the sample of the media corresponding tothe actual frequency spectrum if spillover did occur.
 12. The apparatusas defined in claim 10, wherein the frequency spectrum analyzer isfurther to: determine whether the actual frequency spectrum represents asound not associated with the media; and when the actual frequencyspectrum represents the sound not associated with the media, discard thesample of the media corresponding to the actual frequency.
 13. Theapparatus as defined in claim 8, wherein the expected frequency spectrumis calculated prior to determining the actual frequency spectrum of themedia.
 14. The apparatus as defined in claim 8, wherein determining thatspillover occurred indicates that the meter is in a different room fromthe media presentation device.
 15. A tangible computer readable storagemedium comprising instructions that, when executed, cause a computingdevice to: determine an actual frequency spectrum of the media monitoredby a meter; determine absolute values of differences between amplitudesof corresponding frequency components of the actual frequency spectrumand an expected frequency spectrum, the expected frequency spectrumstored in a database in association with a media identifiercorresponding to the media; determine whether spillover occurred basedon a summation of the absolute values satisfying a threshold; and creditthe media with a media exposure if spillover did not occur.
 16. Thetangible computer readable storage medium as defined in claim 15,wherein the meter is a portable people meter to be worn or carried by apanelist.
 17. The tangible computer readable storage medium as definedin claim 15, wherein the actual frequency spectrum is based on a sampleof the media presented by the media presentation device and collected bythe meter.
 18. The tangible computer readable storage medium as definedin claim 17, further including instructions that, when executed, causethe computing device to discard the sample of the media corresponding tothe actual frequency spectrum if spillover did occur.
 19. The tangiblecomputer readable storage medium as defined in claim 15, wherein theexpected frequency spectrum is calculated prior to determining theactual frequency spectrum of the media.
 20. The tangible computerreadable storage medium as defined in claim 15, wherein determining thatspillover occurred indicates that the meter is in a different room fromthe media presentation device.